library(dplyr)
library(lubridate)
library(tidyverse)
library(plotly)
data_url <- "https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2018/2018-10-23/movie_profit.csv" # this allows us to pull data from the CSV file from the TidyTuesday repository URL
Movies <- read.csv(data_url)I happen to love horror movies so I wanted to see the top 13 (why 13? Ode to Friday the 13th) most profitable movies based on their domestic gross minus their production budget to give us an actual profit amount. This required to filter data to select only certain columns as seen below and filtering only horror movies since there are all types of genres and 3400+ rows in original data set. I also had to change the order of the dates to show year first so I could filter out the top 10 in the past fifty years.
Horror_Movies <- Movies %>%
select(release_date, movie, production_budget, domestic_gross, genre) %>% # this allows us to select the desired columns
filter(genre == "Horror") %>% # this allows us to filter by genre, keeping only horror movies.
mutate(release_date = as.Date(release_date, format = "%m/%d/%Y")) %>%
mutate(release_date = format(release_date, "%Y/%m/%d")) %>% ## these two lines allows us to re-format date from mm/dd/yyyy -> E
filter(release_date >= 1968 & release_date <= 2018) %>%
select(-release_date) %>% #This allows us to filter out data and select 50 years for comparison 1968-2018 which is where our data ends.
mutate(profit = domestic_gross - production_budget)
Horror_Movies <- Horror_Movies %>%
mutate(percentage_profit = ceiling((profit / production_budget) * 100)) %>% # this allows us to calculate the percentage profit by using the information that has been calculated above, this is the percentage I will show on graph and how the data will be represented in descending order.
top_n(n = 13, wt = percentage_profit) #this filters our data to only include the top 13 movies based on the percentage profit.
Horror_Movies## movie production_budget domestic_gross genre profit
## 1 Get Out 5000000 176040665 Horror 171040665
## 2 Split 5000000 138141585 Horror 133141585
## 3 Paranormal Activity 2 3000000 84752907 Horror 81752907
## 4 The Last Exorcism 1800000 41034350 Horror 39234350
## 5 Insidious 1500000 54009150 Horror 52509150
## 6 Saw 1200000 55968727 Horror 54768727
## 7 The Devil Inside 1000000 53262945 Horror 52262945
## 8 Unfriended 1000000 32789645 Horror 31789645
## 9 The Blair Witch Project 600000 140539099 Horror 139939099
## 10 Friday the 13th 550000 39754601 Horror 39204601
## 11 Paranormal Activity 450000 107918810 Horror 107468810
## 12 Maniac 350000 10000000 Horror 9650000
## 13 Halloween 325000 47000000 Horror 46675000
## percentage_profit
## 1 3421
## 2 2663
## 3 2726
## 4 2180
## 5 3501
## 6 4565
## 7 5227
## 8 3179
## 9 23324
## 10 7129
## 11 23882
## 12 2758
## 13 14362I have been trying to practice different ways to represent data and I thought a pie graph was kinda neat. I know they’re not your favorite and I certainly would not use it to represent important data, but I thought it was a good way to see this data in a fun way and be able to interact with it. You can click on titles in legend to remove or add them to the graph to visualize the smaller amounts better.
# Format the percentage values without decimals and with percentage sign
formatted_percentages <- paste0(round(Horror_Movies$percentage_profit), "%")
# Create the pie chart using plotly
p <- plot_ly(Horror_Movies, labels = ~movie, values = ~percentage_profit,
type = "pie",# type of graph we want to make
textinfo = "label+text", #what we want to visibly include on our graph
text = ~paste0(formatted_percentages, "<br>"), #include percentages next to name on graph
hovertemplate = "%{label}<br>%{text}<extra></extra>",#this allows us to decide what information is displayed when we hover over a piece of the pie
marker = list(colors = c("#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#FFFF33", "#A65628", "#F781BF", "#999999", "#66C2A5", "#FFA07A", "#00CED1", "#800080"))) %>% # List of colors for eachmovie
layout(title = "Top 13 Percent Profit Horror Movies Between 1968-2018", #layout is used to name graph, control legend, margins, height and width of graph
showlegend = TRUE, # this allows us to see the legend
margin = list(t = 50, b = 500), # this allows us to adjust the margins...I had to look this one up because my graph was cutting off at the bottom which was fixed with adjusing the margin at the top and bottom and also adjusting the height and width below.
height = 900, # this allows us to adjust the height of the pie chart
width = 800) # this allows us to adjust the width of the pie chart## Warning: Specifying width/height in layout() is now deprecated.
## Please specify in ggplotly() or plot_ly()# Display the interactive pie chart
p